while everyone is somehow still stuck on and fascinated by claude, heres your quick update on the sota of coding models and harnesses mid august 2026
codex is good, both cli and desktop app, you get lots of usage on any plan.
sol is good! and gets the job done, write or dictate a very long and thoughtful prompt, and leave sol xhigh or max fast working on it for an hour or so
omp is an amazing harness, any feature claude code or codex is adding has likely already been here for a couple months.
good harness which im suggesting to all my developer friends, but for everyone else codex is the better option due to its simplicity and being the plug and play option
claude is decent, but not great. all models are somehow getting restrictive. you get basically unlimited opus on max plans, fable is good but slow and the random guardrails suck soo much which is why i havent used it once in weeks now.
gemini 3.7 is great for speed. everyone is sleeping on it, including even me
kimi k3 - great for frontend, one of the few models thats willing to commit crimes for you AND has the intelligence to have a chance at actually succeeding;
ds pro and flash are fast but not something id actually use for important things, unlike sol, fable and maybe 3.7 here and there
glm 5.3 i haven't tested yet
honorable mention to local models which are actually getting good now! 5090s will continue to get more and more expensive in the coming months. sadly.
theres way way more than claude in this world and its taking people surprisingly long to figure that out. maybe its for the best!
My token usage on Claude models has dropped by 83% over the last month - I'm pretty much only using it for quick one off questions or reading papers. it feels impossible for me to get Opus models to stop entering into cyclic loops, and my work is too security adjacent for Fable.
Codex has been an excellent workhorse - doesn't feel like I have to dance around the guardrails, doesn't lose _everything_ when it compacts, and doesn't litter the workspace with a million and one planning to plan files.
Speaking of crimes, the guy behind Grok not only manipulates it for propaganda purposes, but also he's the guy responsible for this: https://www.doge-impact.org/
Anyone using Sam Altman's OpenAI is making a poor ethical decision, but anyone using Grok is, objectively, supporting a monster.
> ds pro and flash are fast but not something id actually use for important things, unlike sol, fable and maybe 3.7 here and there
As someone who's used Gemini 3.7 Flash (Google sub mostly for the storage) and DS4 Flash a lot (~6B tokens), I'd actually place DS4 Flash (even pre-0713) above Gemini 3.7 Flash. Gemini has a tendency to leave some things unimplemented; perhaps it's agy which frankly leaves a bit to be desired as a harness.
Although I will praise DS4 Flash any day, it no longer makes sense for me after the price increase (GPT 5.6 Luna is a much better price point) and I have completely migrated my high volume workflows to Muse Spark 1.2 Contributor (which I find to perform better than DS4 Flash 0713, happily).
> gemini 3.7 is great for speed. everyone is sleeping on it
Is this Gemini 3.7 Flash by any chance? Then - No. Not sleeping on it. It’s just not good.
I had a Python package build fail this week due to an unpinned dependency. Gave it to Gemini spent 5-7mins before I noticed it going off in some tangent. Reran with Claude Opus 4.8 - fixed in under a minute.
I know anecdata of one. But something like this has happened every time I test a new model from Google.
the future is here and one should be thankful for its slightly uneven distribution. otherwise we would hardly have anything left about which to develop strong opinions!
I only have the $20 claude subscription. Last weekend I was doing a fairly heavy task (porting quake to raspberry pi native GLES 1.0) and ran out of claude usage twice, then hooked in with Luna in OpenCode and finished everything off for like $0.40 in tokens, which was impressive.
With Opus 5.0 being kinda crappy vs 4.8, I think Anthropic is in trouble.
On the flip side, I've been using Fable to statically recompile a game binary from a system that's never been emulated to modern C++ while also keeping the code clean, maintainable, and portable and it's been a dream. I'm in awe of how fast Fable is able to bring a 20 year old game that's largely been lost to the sands of time to SDL3. It finished the recomp in about 3 hours. The next day has been fully rewriting the functions to remove old hardware-specific quirks and then rewriting large swaths into multiple classes, subsystems, building tests, etc. Tomorrow I'm going to add mod support and then experiment with a random platform like making a Switch build of the game.
It's expensive but it's doing in hours what no one's done in 2 decades.
Model performance is very much subjective to what you’re using it for. I’m a PM and doing mostly knowledge work, and I’ve been really happy with Opus 5 as my daily driver with very good results, perhaps my favorite anthropic model so far. I do a lot of front-end coding with it too. I prefer it over the openAI models, been switching frequently. Kimi K3 is great too.
I’m sure it depends on the type of work, but for mine, Codex is much more helpful. Honestly, it mostly comes down to it being significantly faster, probably because as many have said, it seems tuned to not spit out word vomit, both in its chat interactions, and its code (Claude is obsessed with massive comment blocks that are basically guaranteed to become dead context noise if you ever use it to iterate on code).
I mostly do very obsessive, tightly scoped, carefully thought out small changes on a fairly boring stack, one interaction at a time, verifying functionality and code. I know what I am doing, but I also know what I don’t like doing (the same exact set of things I’ve already done a dozen times in my career)
> It felt to me that Codex created a much simpler solution in terms of code architecture than Claude.
Wow, I made exactly the opposite experience. Codex loves to make things as complicated as possible, even ignoring instructions and predefined skills. Claude behaves way more pragmatic. Maybe depends on the type of work one does, or even which programming languages/frameworks are used?
I have the same experience. I have passed Codex's code over to Claude (Sonnet mostly) to simplify it and it did a so much better job. Codex couldn't comprehend a simpler solution no matter what.
This post needs an edit. Author is not comparing "Codex" and "Claude". They are comparing Codex TUI/CLI with (presumably) gpt-5.6-sol, against Claude Code TUI/CLI with (presumably) Claude-Opus-5.
Ctrl + f > [5.6, sol, sonnet, opus or fable] yields no results.
"Claude" is a product family, which includes Models, and Harnesses (and probably more). "Claude code" covers both the Claude Code TUI, and CC in the Claude desktop app.
"Codex" is the same, and could refer to the Codex TUI, or Codex in the ChatGPT (formerly codex) desktop app. (And well, historically, gpt-5.*-codex.)
Hearing "Yea Claude is great for coding" takes an hour off my life.
Something something "Honey why don't you finish up with your Nintendo and come to dinner?"
When discussing Claude vs. Codex, etc. I find it necessary to make the distinction between the models and the harness.
Claude's models in my experience do a better job of inferring my intent, or to say it does a better job of giving me the result I imagined in my mind. A recent example was a UI prototype I was building for a desktop application. I had asked GPT's 5.6 Sol to update the open document in the prototype to better reflect the context of the feature I was designing, and 5.6 Sol took it very literally and had just added some text to the currently open document, not what I had in mind. I tried again with Claude Opus 5 and it added a completely new tab with a complete new document that, although imperfect, much better matched my expectations.
You could say this was a prompting skill issue, but seeing how many people are prompting their AI I believe the labs are incentivized to continue to improve their ability to infer intent.
When it comes to the desktop applications though, I find Claude Desktop's output to be incredibly verbose and full of jargon. I feel like it hits me with an entire essay and the UI doesn't have enough typographic hierarchy to make it easy to scan. ChatGPT Desktop is much better in this regard, I feel the output is concise, clear, and gives me just enough info to feel in the loop without being overwhelmed. Even though I have the setting on for technical language, it feels more understandable than Claude. I also feel that ChatGPT's desktop app has a better design and much more polish.
I do not really like how bloated both applications have become though. This weird segmentation of Chat, Work, and Code all just seems like it's pushing a technical limitation onto the user. The other day I opened a document in ChatGPT and asked it to do something, then it told me it could only do it in work "mode", so it then created an entirely new conversation with a reference to the previous conversation. It wasn't a completely new area of the UI either, it just added a "Work" badge to the new conversation in the list. Feels a bit unnecessary, like couldn't you just keep it all within the same conversation?
> Codex feels more like a version of Data from Star Trek
Great analogy for some reason. At fist I felt Codex Sol was a bit more cold. But now that I've worked with it for several weeks it has grown on me, even shown some personality. I appreciate that it is a bit more business-like, Fable is a bit too friendly sometimes when it ought to be focused on work. Codex can be a bit more nit-picky.
I agree with most of his other observations. I've already started to bin tasks based on which model I feel is best suited. In general, for well scoped and straight ahead tasks where banging out code is what I want I reach for Codex. For less specced tasks where I need a broader view and want the model to fill in more details I reach for Fable.
Both are great and they make a good team together.
I've been using Zed mostly with Codex session windows and its great. It is much easier to handle multiple sessions than in the Codex VSCode app and the interface is quite good.
I’ve been experimenting with this for a while, and right now I’m using Luna xhigh as my default. Previously I was using Sol medium.
Sol medium is a great balance between speed and being thorough, but it’s quite expensive. Luna xhigh seems to compensate for slightly lower intelligence by thinking and reasoning for longer, so tasks can take more time to complete. But it’s crazy cheap.
I also have some custom evals using promptfoo to make sure I’m not introducing regressions when switching models. So far, Luna xhigh has been really, really good for the price.
Sol is for routine work, Opus for frontend/design, and Fable for more complex / ambiguous / architecture work. Fable works extremely well to drive Sol as a subagent.
Fable is the only one you can actually trust to not look at the code, but Sol is somehow still more pleasant to work with, especially in fast mode. Opus is the enemy, and it will make you insane if you talk to it for too long.
For me, codex $100 mo/plan and a claude teams account at work (mostly sonnet, some opus), Claude basically feels about as effective as Codex did 4-5 months ago pre-5.6. Claude still has weird patterns of being confident in one answer while another chat with the same model is confident in another answer, where one answer is clearly wrong. Missed details, over-engineering in places, while still overall helpful and effective. Codex, however, just feels freaking rock solid on Sol high. I literally have zero complaints.
"How this article was written
I wrote this article and used Grammarly to proofread and fix it."
What a brave new world we're in, where this is necessary. Regardless, it's appreciated. Although, I have the feeling that those using an LLM to do most of their writing will be less likely to include such a disclaimer.
The speed is the first big contrast; I have a routine multi-step skill that I run several of per week. Opus 5 was routinely taking 2 hours to do it, while older Claude models took around 20 mins; Codex restored that speed.
Second is legibility. Somebody wrote in one of the related discussions yesterday that Claude's current linguistic contortions could legitimately be considered damaging to mental health, which doesn't seem (too) hyperbolic to me. Codex (Sol) isn't perfect but it's much more direct. And so far I haven't seen it display much of an attitude, vs Opus's infuriating passive aggressive sulky know it all personality.
I slightly prefer Anthropic to OpenAI as a company, but I will vote with my wallet and discontinue my max subscription unless Anthropic does some serious damage control within the next week or two.
I find Claude more often gets my intent without having to spell things out for it, while Codex gets hung up on minor details and over-engineers a solution for them.
Claude can add comments in code that are better just living as a comment on the PR.
You'll ask it to do something and it'll comment the code with an answer to what you asked it, rather than just explanatory comments to whoever comes after.
There's also a second issue that if the code is actually incorrect, the comment can nevertheless bolster the case for it.
I found that Claude would litter the codebase with what felt like notes-to-self. Sometimes it would make an abstraction that wasn't very well thought out and when I pushed back on this choice, it would usually go with something more along the lines of what I suggested in my pushback, but left a comment like "use the git CLI instead of re-implementing git" or something.
It felt like it was commenting on the diff sometimes instead of what the code was doing.
Those verbose comments provide context for why something is there way it is, so it can take those decisions in account when making changes in the future.
It writes out stories describing what isn't there or what used to be there. It's usually not helpful, just noise. It also likes to write it in very verbose AI-styled prose.
And the way they are written is rather unhelpful: '//this not cosmetic: this css helps the user understand where he should click'. The 'this is not' pattern is useless at best.
If they have in mind what I think of when I think of claude comments then frequently they are only relevant to the back and forth that produced the code and are exaggerated to the point of being misleading. A very common example is to land on something that isn't quite right or doesn't quite work, prompt an adjustment, and then the adjustment will have some grandiose prose about "preventing the critical bug that shipped once." People just need to clean up such litter manually but it would be nice if the models generated less of it.
The story of how the code got to a certain state belongs primarily in git commit messages, not in comments. Comments should serve to aid in understanding of the code, highlight footguns or hidden dependencies, etc.
Claude very often litters code with comments about decisions that were made within a single session/pull request, its just noise.
If one needs to dig history, then git log -p and similar are way more useful and reflect the real history rather than LLM inference about it. Comments reflecting the goal are more useful as neither history nor code itself covers it.
Those massive comments generally refer to intermediate states that never shipped, making them utterly useless, noisy and confusing as a permanent artifact. They're sometimes useful to reviewers, so a commit message or pull request comment is much more appropriate.
I think a part of this is that people tend to undervalue their own skills and expertise when talking about these anecdotes.
A lot of people in the comments do have a software engineering background. People at different skill levels in different backgrounds are going to be using these tools in different ways, and that's going to heavily impact their experiences with these models.
Sure, there are differences between Fable and Sol. But I've even seen people on here saying that they're getting better mileage out of Qwen models they're self hosting.
I think the driver is just as important than the car, when it comes to this sort of stuff.
I think this definitely applies to fable/opus/sol, mixed with the undeterministic nature of the models. But those claiming Qwen are just outright coping. Thats nonsense imo
I've noticed that too with Sol (xhigh) it starts with pretty good architectural goals, but really get lost in the weeds with certification and validation. Like it won't trust native tools like (in my case packer, Ansible, gcloud) to fail reliably under possible error conditions, and insists on implementing custom verification.
It also doesn't have a clear idea of what the actual threat model is, and builds all kinds of extremely defensive systems to account for imagined hostile actors. I'm like "Dude, it's only our systems that are creating these SVGs, they're never going to be user supplied, so you don't need to write an entire validation and sanitation framework here."
It also seems to treat the desired initial state of something as a permanent invariant and designs elaborate tests to ensure that it remains that way. Then when you make one little change it has to go and update a ton of tests it created.
I've had to rip out a bunch of overengineered jank from several feature implementations, and in doing so I ended up having to create retrospective documents that warn against this kind of behavior that I'll have the model review whenever a plan begins to go sideways.
What, you don't like it proposing sha256 checksums and state enums for everything?? :D
It does seem to have this predilection, but I find it calms down if I tell it to simplify for "this phase". Sure, we'll pick that up down the road.. I promise. :D
I broadly agreed with the authors experience, although I wouldn't say codex does anything "wrong". I think different agents/LLM's have different personalities, and it takes learning to understand how to get them to do what you want. I had the same experience when we started using claude at work, and I was trained on codex. Claude seemed to do everything "wrong", because I was writing instructions designed for codex.
I do agree claude looks for more things to do in your repo, whereas codex is more likely to do what its old and stop. Which is better is personal preference as far as I can tell.
>> I think the main difference I feel between Claude and Codex is that Claude tries to go above and beyond what is asked and guess what you might want and then directly do it, while Codex is more like a companion that does what you tell it but will not overdo it. It will stop at the first sign that it might be done.
tl;dr I gave GPT 5.6 a small-medium sized ticket, which should have been several hundred lines plus tests. It ended up creating a 25,000+ line diff. Another GPT 5.6 Sol with fresh context looked at the worktree and said 98% of it should be thrown away. Claude thought the same, and suggested that several dozen compactions the model went through over several hours must have caused it to go adrift. I guess that's one consequence of having a relatively small context window.
I still use Sol quite a bit. I find that it's consistently the opposite of what the author describes: it's too relentless. It doesn't know when to stop. Opus is the opposite: it'll give up a bit too easily. If everything goes well that's not an issue, but often times it'll say things like "task is done, btw I couldn't do X Y Z" and X Y Z will be some important verification step that failed because another agent was using that resource or something.
At this point I trust GPT 5.6 mostly with surgical changes, or general codebase exploration tasks. It is a faster model, so it's easier to get small things done with it. For everything else I prefer Claude, despite its annoying tendencies.
while everyone is somehow still stuck on and fascinated by claude, heres your quick update on the sota of coding models and harnesses mid august 2026
codex is good, both cli and desktop app, you get lots of usage on any plan. sol is good! and gets the job done, write or dictate a very long and thoughtful prompt, and leave sol xhigh or max fast working on it for an hour or so
omp is an amazing harness, any feature claude code or codex is adding has likely already been here for a couple months. good harness which im suggesting to all my developer friends, but for everyone else codex is the better option due to its simplicity and being the plug and play option
claude is decent, but not great. all models are somehow getting restrictive. you get basically unlimited opus on max plans, fable is good but slow and the random guardrails suck soo much which is why i havent used it once in weeks now.
gemini 3.7 is great for speed. everyone is sleeping on it, including even me
kimi k3 - great for frontend, one of the few models thats willing to commit crimes for you AND has the intelligence to have a chance at actually succeeding;
ds pro and flash are fast but not something id actually use for important things, unlike sol, fable and maybe 3.7 here and there
glm 5.3 i haven't tested yet
honorable mention to local models which are actually getting good now! 5090s will continue to get more and more expensive in the coming months. sadly.
theres way way more than claude in this world and its taking people surprisingly long to figure that out. maybe its for the best!
My token usage on Claude models has dropped by 83% over the last month - I'm pretty much only using it for quick one off questions or reading papers. it feels impossible for me to get Opus models to stop entering into cyclic loops, and my work is too security adjacent for Fable.
Codex has been an excellent workhorse - doesn't feel like I have to dance around the guardrails, doesn't lose _everything_ when it compacts, and doesn't litter the workspace with a million and one planning to plan files.
I think harness/model pairs matter more than your analysis lets on.
I've had great luck with the ds flash v4, paired with prime-agent for the harness--I like the results a lot. And you get to see thinking tokens.
I haven't liked the model as much in opencode.
Sol & luna have been great everywhere. sol plans, luna builds.
Prime Agent looks really interesting. Both the "recursive language model" bit and routing everything through IPython.
https://github.com/PrimeIntellect-ai/prime-agent
Not mentioning Grok 4.6 here is a crime. Fast and accurate.
And it can communicate, unlike the gobbledygook that comes out of Claude.
> Not mentioning Grok 4.6 here is a crime.
Not yet. Don't give the guy ideas.
Speaking of crimes, the guy behind Grok not only manipulates it for propaganda purposes, but also he's the guy responsible for this: https://www.doge-impact.org/
Anyone using Sam Altman's OpenAI is making a poor ethical decision, but anyone using Grok is, objectively, supporting a monster.
Been working a lot recently with Grok 4.6 for implementation and gpt 5.6 sol for review. Worked really good so far.
I would not use Grok if it paid me per token… wild wild stuff…
> ds pro and flash are fast but not something id actually use for important things, unlike sol, fable and maybe 3.7 here and there
As someone who's used Gemini 3.7 Flash (Google sub mostly for the storage) and DS4 Flash a lot (~6B tokens), I'd actually place DS4 Flash (even pre-0713) above Gemini 3.7 Flash. Gemini has a tendency to leave some things unimplemented; perhaps it's agy which frankly leaves a bit to be desired as a harness.
Although I will praise DS4 Flash any day, it no longer makes sense for me after the price increase (GPT 5.6 Luna is a much better price point) and I have completely migrated my high volume workflows to Muse Spark 1.2 Contributor (which I find to perform better than DS4 Flash 0713, happily).
> gemini 3.7 is great for speed. everyone is sleeping on it
Is this Gemini 3.7 Flash by any chance? Then - No. Not sleeping on it. It’s just not good.
I had a Python package build fail this week due to an unpinned dependency. Gave it to Gemini spent 5-7mins before I noticed it going off in some tangent. Reran with Claude Opus 4.8 - fixed in under a minute.
I know anecdata of one. But something like this has happened every time I test a new model from Google.
the future is here and one should be thankful for its slightly uneven distribution. otherwise we would hardly have anything left about which to develop strong opinions!
I only have the $20 claude subscription. Last weekend I was doing a fairly heavy task (porting quake to raspberry pi native GLES 1.0) and ran out of claude usage twice, then hooked in with Luna in OpenCode and finished everything off for like $0.40 in tokens, which was impressive.
With Opus 5.0 being kinda crappy vs 4.8, I think Anthropic is in trouble.
On the flip side, I've been using Fable to statically recompile a game binary from a system that's never been emulated to modern C++ while also keeping the code clean, maintainable, and portable and it's been a dream. I'm in awe of how fast Fable is able to bring a 20 year old game that's largely been lost to the sands of time to SDL3. It finished the recomp in about 3 hours. The next day has been fully rewriting the functions to remove old hardware-specific quirks and then rewriting large swaths into multiple classes, subsystems, building tests, etc. Tomorrow I'm going to add mod support and then experiment with a random platform like making a Switch build of the game.
It's expensive but it's doing in hours what no one's done in 2 decades.
Which one?
Model performance is very much subjective to what you’re using it for. I’m a PM and doing mostly knowledge work, and I’ve been really happy with Opus 5 as my daily driver with very good results, perhaps my favorite anthropic model so far. I do a lot of front-end coding with it too. I prefer it over the openAI models, been switching frequently. Kimi K3 is great too.
I’m sure it depends on the type of work, but for mine, Codex is much more helpful. Honestly, it mostly comes down to it being significantly faster, probably because as many have said, it seems tuned to not spit out word vomit, both in its chat interactions, and its code (Claude is obsessed with massive comment blocks that are basically guaranteed to become dead context noise if you ever use it to iterate on code).
I mostly do very obsessive, tightly scoped, carefully thought out small changes on a fairly boring stack, one interaction at a time, verifying functionality and code. I know what I am doing, but I also know what I don’t like doing (the same exact set of things I’ve already done a dozen times in my career)
> It felt to me that Codex created a much simpler solution in terms of code architecture than Claude.
Wow, I made exactly the opposite experience. Codex loves to make things as complicated as possible, even ignoring instructions and predefined skills. Claude behaves way more pragmatic. Maybe depends on the type of work one does, or even which programming languages/frameworks are used?
I have the same experience. I have passed Codex's code over to Claude (Sonnet mostly) to simplify it and it did a so much better job. Codex couldn't comprehend a simpler solution no matter what.
Im trying to do my part in the world here.
This post needs an edit. Author is not comparing "Codex" and "Claude". They are comparing Codex TUI/CLI with (presumably) gpt-5.6-sol, against Claude Code TUI/CLI with (presumably) Claude-Opus-5.
Ctrl + f > [5.6, sol, sonnet, opus or fable] yields no results.
"Claude" is a product family, which includes Models, and Harnesses (and probably more). "Claude code" covers both the Claude Code TUI, and CC in the Claude desktop app.
"Codex" is the same, and could refer to the Codex TUI, or Codex in the ChatGPT (formerly codex) desktop app. (And well, historically, gpt-5.*-codex.)
Hearing "Yea Claude is great for coding" takes an hour off my life.
Something something "Honey why don't you finish up with your Nintendo and come to dinner?"
When discussing Claude vs. Codex, etc. I find it necessary to make the distinction between the models and the harness.
Claude's models in my experience do a better job of inferring my intent, or to say it does a better job of giving me the result I imagined in my mind. A recent example was a UI prototype I was building for a desktop application. I had asked GPT's 5.6 Sol to update the open document in the prototype to better reflect the context of the feature I was designing, and 5.6 Sol took it very literally and had just added some text to the currently open document, not what I had in mind. I tried again with Claude Opus 5 and it added a completely new tab with a complete new document that, although imperfect, much better matched my expectations.
You could say this was a prompting skill issue, but seeing how many people are prompting their AI I believe the labs are incentivized to continue to improve their ability to infer intent.
When it comes to the desktop applications though, I find Claude Desktop's output to be incredibly verbose and full of jargon. I feel like it hits me with an entire essay and the UI doesn't have enough typographic hierarchy to make it easy to scan. ChatGPT Desktop is much better in this regard, I feel the output is concise, clear, and gives me just enough info to feel in the loop without being overwhelmed. Even though I have the setting on for technical language, it feels more understandable than Claude. I also feel that ChatGPT's desktop app has a better design and much more polish.
I do not really like how bloated both applications have become though. This weird segmentation of Chat, Work, and Code all just seems like it's pushing a technical limitation onto the user. The other day I opened a document in ChatGPT and asked it to do something, then it told me it could only do it in work "mode", so it then created an entirely new conversation with a reference to the previous conversation. It wasn't a completely new area of the UI either, it just added a "Work" badge to the new conversation in the list. Feels a bit unnecessary, like couldn't you just keep it all within the same conversation?
> Codex feels more like a version of Data from Star Trek
Great analogy for some reason. At fist I felt Codex Sol was a bit more cold. But now that I've worked with it for several weeks it has grown on me, even shown some personality. I appreciate that it is a bit more business-like, Fable is a bit too friendly sometimes when it ought to be focused on work. Codex can be a bit more nit-picky.
I agree with most of his other observations. I've already started to bin tasks based on which model I feel is best suited. In general, for well scoped and straight ahead tasks where banging out code is what I want I reach for Codex. For less specced tasks where I need a broader view and want the model to fill in more details I reach for Fable.
Both are great and they make a good team together.
I've been using Zed mostly with Codex session windows and its great. It is much easier to handle multiple sessions than in the Codex VSCode app and the interface is quite good.
Which models? It is not useful to compare harnesses without this information.
Which models, and at what effort level?
I’ve been experimenting with this for a while, and right now I’m using Luna xhigh as my default. Previously I was using Sol medium.
Sol medium is a great balance between speed and being thorough, but it’s quite expensive. Luna xhigh seems to compensate for slightly lower intelligence by thinking and reasoning for longer, so tasks can take more time to complete. But it’s crazy cheap.
I also have some custom evals using promptfoo to make sure I’m not introducing regressions when switching models. So far, Luna xhigh has been really, really good for the price.
Don’t sleep on it. Give Luna a try.
Summer 2026 meta (well, mine):
Sol is for routine work, Opus for frontend/design, and Fable for more complex / ambiguous / architecture work. Fable works extremely well to drive Sol as a subagent.
Fable is the only one you can actually trust to not look at the code, but Sol is somehow still more pleasant to work with, especially in fast mode. Opus is the enemy, and it will make you insane if you talk to it for too long.
For me, codex $100 mo/plan and a claude teams account at work (mostly sonnet, some opus), Claude basically feels about as effective as Codex did 4-5 months ago pre-5.6. Claude still has weird patterns of being confident in one answer while another chat with the same model is confident in another answer, where one answer is clearly wrong. Missed details, over-engineering in places, while still overall helpful and effective. Codex, however, just feels freaking rock solid on Sol high. I literally have zero complaints.
"How this article was written I wrote this article and used Grammarly to proofread and fix it."
What a brave new world we're in, where this is necessary. Regardless, it's appreciated. Although, I have the feeling that those using an LLM to do most of their writing will be less likely to include such a disclaimer.
I did the same this week.
The speed is the first big contrast; I have a routine multi-step skill that I run several of per week. Opus 5 was routinely taking 2 hours to do it, while older Claude models took around 20 mins; Codex restored that speed.
Second is legibility. Somebody wrote in one of the related discussions yesterday that Claude's current linguistic contortions could legitimately be considered damaging to mental health, which doesn't seem (too) hyperbolic to me. Codex (Sol) isn't perfect but it's much more direct. And so far I haven't seen it display much of an attitude, vs Opus's infuriating passive aggressive sulky know it all personality.
I slightly prefer Anthropic to OpenAI as a company, but I will vote with my wallet and discontinue my max subscription unless Anthropic does some serious damage control within the next week or two.
I find Claude more often gets my intent without having to spell things out for it, while Codex gets hung up on minor details and over-engineers a solution for them.
> Changes created by Codex had fewer comments in Ruby/Ruby on Rails code. I liked that a lot, and I will soon share some experiments I ran on this.
Why is fewer comments a good thing?
Claude can add comments in code that are better just living as a comment on the PR.
You'll ask it to do something and it'll comment the code with an answer to what you asked it, rather than just explanatory comments to whoever comes after.
There's also a second issue that if the code is actually incorrect, the comment can nevertheless bolster the case for it.
Yeah, Claude insists on prompt-affirming comments, and often refers to the previous draft or implementation instead of the status quo.
> Claude can add comments in code that are better just living as a comment on the PR.
Not to Claude – its own, old comments have helped me/it solve new issues on more than one occasion.
Claude assumes humans won't look at code anymore. The comments are for the next Claude that comes in to change something.
I found that Claude would litter the codebase with what felt like notes-to-self. Sometimes it would make an abstraction that wasn't very well thought out and when I pushed back on this choice, it would usually go with something more along the lines of what I suggested in my pushback, but left a comment like "use the git CLI instead of re-implementing git" or something.
It felt like it was commenting on the diff sometimes instead of what the code was doing.
Claude comments look like this
//add returns the sum of x and y
//per section 2.1 of addition-implementation-plan.md sum is designed as the seam for user addition interfaces.
//previously sum added numbers, now it adds numbers
def add(x, y):
just reading this comments made me despise the fact that I have to read this shit again tomorrow
It's really time to move to OpenAI...
I’m trying to pitch my boss on something like bedrock/foundry/vertex where we can just swap the model and still have it serverless over an api.
Digital ocean particularly looks promising as well.
Claude is creating verbose comments with recent models and people are are tired of it
Those verbose comments provide context for why something is there way it is, so it can take those decisions in account when making changes in the future.
> why something is there way it is
It writes out stories describing what isn't there or what used to be there. It's usually not helpful, just noise. It also likes to write it in very verbose AI-styled prose.
"don't write a goddamn novel" <- I've said this to claude way too many times, nothing you can do actually seems to make it significantly less verbose
And the way they are written is rather unhelpful: '//this not cosmetic: this css helps the user understand where he should click'. The 'this is not' pattern is useless at best.
If they have in mind what I think of when I think of claude comments then frequently they are only relevant to the back and forth that produced the code and are exaggerated to the point of being misleading. A very common example is to land on something that isn't quite right or doesn't quite work, prompt an adjustment, and then the adjustment will have some grandiose prose about "preventing the critical bug that shipped once." People just need to clean up such litter manually but it would be nice if the models generated less of it.
The story of how the code got to a certain state belongs primarily in git commit messages, not in comments. Comments should serve to aid in understanding of the code, highlight footguns or hidden dependencies, etc.
Claude very often litters code with comments about decisions that were made within a single session/pull request, its just noise.
If one needs to dig history, then git log -p and similar are way more useful and reflect the real history rather than LLM inference about it. Comments reflecting the goal are more useful as neither history nor code itself covers it.
Those massive comments generally refer to intermediate states that never shipped, making them utterly useless, noisy and confusing as a permanent artifact. They're sometimes useful to reviewers, so a commit message or pull request comment is much more appropriate.
Yes I hate when Claude explain in comments how it is now compared to how it was before, or how it could be if a different decision had been taken.
Dude, just talk about the current state of the code!
Claude comments often contain the whole iterative chain of decisions that led to the current state.
Useful for the LLM to know the "why", but not something a human would do, unless it's a very critical and confusing part of the code.
Fewer comments is generally a bad thing.
Fewer AI-generated comments is generally a good thing.
Good code is self-explanatory and don't need comments.
I can really recommend the book Clean Code, here is a summary: https://gist.github.com/wojteklu/73c6914cc446146b8b533c0988c...
I find claude often comments about what the code *does not* do. Relevant at the moment, but doesn't belong in the codebase.
Kinda interesting reading people comment with completely contradictory anecdotal experience with the various models.
I think a part of this is that people tend to undervalue their own skills and expertise when talking about these anecdotes.
A lot of people in the comments do have a software engineering background. People at different skill levels in different backgrounds are going to be using these tools in different ways, and that's going to heavily impact their experiences with these models.
Sure, there are differences between Fable and Sol. But I've even seen people on here saying that they're getting better mileage out of Qwen models they're self hosting.
I think the driver is just as important than the car, when it comes to this sort of stuff.
I think this definitely applies to fable/opus/sol, mixed with the undeterministic nature of the models. But those claiming Qwen are just outright coping. Thats nonsense imo
Agree with most of these.
One thing I don’t love about codex/sol is I find it tends to overengineer and be overly cautious.
I was using it to do create some scraping + data processing.
It went kind of crazy on the provenance, need at least 3 sources of consensus before promoting facts type bullshit.
defined a bunch of enums and gates.
I just wanted scrape some site data and put it into a SQLite dB. Like chill codex.
I feel like Claude is better at that.
I've noticed that too with Sol (xhigh) it starts with pretty good architectural goals, but really get lost in the weeds with certification and validation. Like it won't trust native tools like (in my case packer, Ansible, gcloud) to fail reliably under possible error conditions, and insists on implementing custom verification.
It also doesn't have a clear idea of what the actual threat model is, and builds all kinds of extremely defensive systems to account for imagined hostile actors. I'm like "Dude, it's only our systems that are creating these SVGs, they're never going to be user supplied, so you don't need to write an entire validation and sanitation framework here."
It also seems to treat the desired initial state of something as a permanent invariant and designs elaborate tests to ensure that it remains that way. Then when you make one little change it has to go and update a ton of tests it created.
I've had to rip out a bunch of overengineered jank from several feature implementations, and in doing so I ended up having to create retrospective documents that warn against this kind of behavior that I'll have the model review whenever a plan begins to go sideways.
“Extreme defensiveness” is a good characterization.
I wonder if it’s an artifact of OpenAI’s values or rl training approach.
Also, it prob does make it perform better just not more efficient.
Great for the OpenAI employee working on security scanning who doesn’t have to pay for their tokens.
Not so much for the dev building their web app who is trying maximize their subscription.
Like hiring NASA to build you a shed.
As a follow up.
I feel like codex/sol is better at well scoped hard technical problem.
Where it can sort of run this brute force analytical loop.
Like doing performance optimization or other search type problems. I think the math proofs are good examples of this.
Agreed, only LLM I can give a folder of packet traces from a multiplayer game and wake up the next day with a server that feels identical.
What, you don't like it proposing sha256 checksums and state enums for everything?? :D It does seem to have this predilection, but I find it calms down if I tell it to simplify for "this phase". Sure, we'll pick that up down the road.. I promise. :D
Claude is pretentious abrasive snob, codex is the blue collar guy that gets shit done in my experience.
I broadly agreed with the authors experience, although I wouldn't say codex does anything "wrong". I think different agents/LLM's have different personalities, and it takes learning to understand how to get them to do what you want. I had the same experience when we started using claude at work, and I was trained on codex. Claude seemed to do everything "wrong", because I was writing instructions designed for codex.
I do agree claude looks for more things to do in your repo, whereas codex is more likely to do what its old and stop. Which is better is personal preference as far as I can tell.
>> I think the main difference I feel between Claude and Codex is that Claude tries to go above and beyond what is asked and guess what you might want and then directly do it, while Codex is more like a companion that does what you tell it but will not overdo it. It will stop at the first sign that it might be done.
Damn, my experience is the complete opposite of this. I have posted about it a few times, e.g. https://news.ycombinator.com/item?id=49348265
tl;dr I gave GPT 5.6 a small-medium sized ticket, which should have been several hundred lines plus tests. It ended up creating a 25,000+ line diff. Another GPT 5.6 Sol with fresh context looked at the worktree and said 98% of it should be thrown away. Claude thought the same, and suggested that several dozen compactions the model went through over several hours must have caused it to go adrift. I guess that's one consequence of having a relatively small context window.
I still use Sol quite a bit. I find that it's consistently the opposite of what the author describes: it's too relentless. It doesn't know when to stop. Opus is the opposite: it'll give up a bit too easily. If everything goes well that's not an issue, but often times it'll say things like "task is done, btw I couldn't do X Y Z" and X Y Z will be some important verification step that failed because another agent was using that resource or something.
At this point I trust GPT 5.6 mostly with surgical changes, or general codebase exploration tasks. It is a faster model, so it's easier to get small things done with it. For everything else I prefer Claude, despite its annoying tendencies.
I run sol on medium about 90% of the time. Maybe that's worth a try. Especially for a small change.